System and method to evaluate entity operations across multiple virtual environments

Abstract

A system comprises a memory communicatively coupled to at least one processor. The at least one processor is configured to receive multiple tracked activities comprising one or more actions performed by an entity in a virtual environment and execute the machine learning algorithm to determine intents based on the tracked activities, generate synthetic network structures based on the determined intents, assign adverse impacts to the synthetic network structures, and place the synthetic network structures in the virtual environment. The processor is configured to present, to the entity, access to the synthetic network structures in the virtual environment, generate reports comprising that the entity is associated with the corresponding adverse impacts over in response to determining that the entity performed one or more actions in association with the synthetic network structures, and train the one or more machine learning models using the one or more reports.

1 . A system, comprising:
 a memory operable to store:
 a machine learning algorithm configured, when executed, to evaluate data in accordance with one or more machine learning models; and 
   at least one processor communicatively coupled to the memory and configured to:
 receive a first plurality of tracked activities comprising one or more actions performed by a first entity in a first virtual environment over a first period of time, wherein:
 the first entity is associated with a first electronic attacker; and 
 the first virtual environment is an isolated partition of a communication network; 
 
 execute the machine learning algorithm to:
 determine a first determined intent based on a first activity of the first plurality of tracked activities; 
 generate a first synthetic network structure based on the first determined intent, the first synthetic network structure comprising a first plurality of synthetic elements configured to resemble a first plurality of network resources; 
 assign a first adverse impact to the first synthetic network structure; 
 place the first synthetic network structure in the first virtual environment; 
 determine a second determined intent based on a second tracked activity of the first plurality of tracked activities; 
 generate a second synthetic network structure based on the second determined intent, the second synthetic network structure comprising a second plurality of synthetic elements configured to resemble a second plurality of network resources; 
 assign a second adverse impact to the second synthetic network structure; and 
 place the second synthetic network structure in the first virtual environment; 
 
 present, to the first entity, access to the first synthetic network structure and the second synthetic network structure in the first virtual environment, wherein:
 the first synthetic network structure is associated with a first divergent path in the first virtual environment; and 
 the second synthetic network structure is associated with a second divergent path in the first virtual environment; 
 
 determine whether the first entity performed a first action in association with the first synthetic network structure or a second action in association with the second synthetic network structure; 
 in response to determining that the first entity performed the first action in association with the first synthetic network structure, generate a first report comprising that the first entity is associated with the first adverse impact over the first period of time; 
 in response to determining that the first entity performed the second action in association with the second synthetic network structure, generate a second report comprising that the first entity is associated with the second adverse impact over the first period of time; and 
 train the one or more machine learning models using the first report and the second report. 
   
     
     
         2 . The system of  claim 1 , wherein the at least one processor is further configured to:
 receive a second plurality of tracked activities comprising one or more additional actions performed by the first entity in the first virtual environment over a second period of time;   execute the machine learning algorithm to:
 determine a third determined intent based on a third activity of the second plurality of tracked activities; 
 generate a third synthetic network structure based on the third determined intent, the third synthetic network structure comprising a third plurality of synthetic elements configured to resemble a third plurality of network resources; 
 assign a third adverse impact to the third synthetic network structure; 
 place the third synthetic network structure in the first virtual environment; 
 determine a fourth determined intent based on a fourth tracked activity of the second plurality of tracked activities; 
 generate a fourth synthetic network structure based on the fourth determined intent, the fourth synthetic network structure comprising a fourth plurality of synthetic elements configured to resemble a fourth plurality of network resources; 
 assign a fourth adverse impact to the fourth synthetic network structure; and 
 place the fourth synthetic network structure in the first virtual environment; 
   present, to the first entity, access to the third synthetic network structure and the fourth synthetic network structure in the first virtual environment, wherein:
 the third synthetic network structure is associated with a third divergent path in the first virtual environment; and 
 the fourth synthetic network structure is associated with a fourth divergent path in the first virtual environment; 
   determine whether the first entity performed a third action in association with the third synthetic network structure or a fourth action in association with the fourth synthetic network structure;
 in response to determining that the first entity performed the third action in association with the third synthetic network structure, generate a third report comprising that the first entity is associated with the third adverse impact; and 
 in response to determining that the first entity performed the fourth action in association with the fourth synthetic network structure, generate a fourth report comprising that the first entity is associated with the fourth adverse impact; and 
   train the one or more machine learning models using the third report and the fourth report.   
     
     
         3 . The system of  claim 1 , wherein the at least one processor is further configured to:
 receive a second plurality of tracked activities comprising one or more additional actions performed by the first entity in the first virtual environment over a second period of time;   execute the machine learning algorithm to:
 determine a third determined intent based on a third activity of the second plurality of tracked activities; 
 generate a third synthetic network structure based on the third determined intent, the third synthetic network structure comprising a third plurality of synthetic elements configured to resemble a third plurality of network resources; 
 assign a third adverse impact to the third synthetic network structure; 
 determine a fourth determined intent based on a fourth tracked activity of the second plurality of tracked activities; 
 generate a fourth synthetic network structure based on the fourth determined intent, the fourth synthetic network structure comprising a fourth plurality of synthetic elements configured to resemble a fourth plurality of network resources; 
 assign a fourth adverse impact to the fourth synthetic network structure; 
 generate a second virtual environment configured to resemble one or more additional portions of the communication network; and 
 place the third synthetic network structure and the fourth synthetic network structure in the second virtual environment; 
   present, to the first entity, access to the third synthetic network structure and the fourth synthetic network structure in the first virtual environment, wherein:
 the third synthetic network structure is associated with a third divergent path in the second virtual environment; and 
 the fourth synthetic network structure is associated with a fourth divergent path in the second virtual environment; 
   determine whether the first entity performed a third action in association with the third synthetic network structure or a fourth action in association with the fourth synthetic network structure;
 in response to determining that the first entity performed the third action in association with the third synthetic network structure, generate a third report comprising that the first entity is associated with the third adverse impact; and 
 in response to determining that the first entity performed the fourth action in association with the fourth synthetic network structure, generate a fourth report comprising that the first entity is associated with the fourth adverse impact; and 
   train the one or more machine learning models using the third report and the fourth report.   
     
     
         4 . The system of  claim 3 , wherein:
 the first virtual environment is controlled by a first server associated with a first organization; and   the second virtual environment is controlled by a second server associated with a second organization.   
     
     
         5 . The system of  claim 1 , wherein the at least one processor is further configured to:
 receive a second plurality of tracked activities comprising one or more additional actions performed by a second entity in a second virtual environment over a second period of time, wherein:
 the second entity is associated with a second electronic attacker; and 
 the second virtual environment is an isolated partition of a communication network; 
   execute the machine learning algorithm to:
 determine a third determined intent based on a third tracked activity of the second plurality of tracked activities; 
 generate a third synthetic network structure based on the third determined intent, the third synthetic network structure comprising a third plurality of synthetic elements configured to resemble a third plurality of network resources; 
 assign a third adverse impact to the third synthetic network structure; 
 place the third synthetic network structure in the second virtual environment; 
 determine a fourth determined intent based on a fourth tracked activity of the second plurality of tracked activities; 
 generate a fourth synthetic network structure based on the fourth determined intent, the fourth synthetic network structure comprising a fourth plurality of synthetic elements configured to resemble a fourth plurality of network resources; 
 assign a fourth adverse impact to the fourth synthetic network structure; and 
 place the fourth synthetic network structure in the second virtual environment; 
   present, to the second entity, access to the third synthetic network structure and the fourth synthetic network structure in the second virtual environment, wherein:
 the third synthetic network structure is associated with a third divergent path in the second virtual environment; and 
 the fourth synthetic network structure is associated with a fourth divergent path in the second virtual environment; 
   determine whether the second entity performed a third action in association with the third synthetic network structure or a fourth action in association with the fourth synthetic network structure;
 in response to determining that the second entity performed the third action in association with the third synthetic network structure, generate a third report comprising that the second entity is associated with the third adverse impact over the second period of time; and 
 in response to determining that the second entity performed the fourth action in association with the fourth synthetic network structure, generate a fourth report comprising that the second entity is associated with the fourth adverse impact over the second period of time; and 
   train the one or more machine learning models using the third report and the fourth report.   
     
     
         6 . The system of  claim 5 , wherein the at least one processor is further configured to:
 receive a second plurality of tracked activities comprising one or more first additional actions performed by the first entity in the first virtual environment over a third period of time;   receive a third plurality of tracked activities comprising one or more second additional actions performed by the second entity in the second virtual environment over a fourth period of time;   execute the machine learning algorithm to:
 determine a fifth determined intent based on a fifth activity of the second plurality of tracked activities; 
 generate a fifth synthetic network structure based on the fifth determined intent, the fifth synthetic network structure comprising a fifth plurality of synthetic elements configured to resemble a fifth plurality of network resources; 
 assign a fifth adverse impact to the fifth synthetic network structure; 
 determine a sixth determined intent based on a sixth tracked activity of the second plurality of tracked activities; 
 generate a sixth synthetic network structure based on the sixth determined intent, the sixth synthetic network structure comprising a sixth plurality of synthetic elements configured to resemble a sixth plurality of network resources; 
 assign a sixth adverse impact to the sixth synthetic network structure; and 
 generate a third virtual environment configured to resemble one or more new additional portions of the communication network; 
   place the fifth synthetic network structure and the sixth synthetic network structure in the third virtual environment;   present, to the first entity, access to the fifth synthetic network structure in the third virtual environment, the fifth synthetic network structure is associated with a fifth divergent path in the third virtual environment;   present, to the second entity, access to the sixth synthetic network structure in the third virtual environment, the sixth synthetic network structure is associated with a sixth divergent path in the third virtual environment;   determine whether the first entity performed a fifth action in association with the fifth synthetic network structure;   in response to determining that the first entity performed the fifth action in association with the fifth synthetic network structure, generate a fifth report comprising that the first entity is associated with the fifth adverse impact;
 determine whether the second entity performed a sixth action in association with the sixth synthetic network structure; and 
 in response to determining that the second entity performed the sixth action in association with the sixth synthetic network structure, generate a sixth report comprising that the second entity is associated with the sixth adverse impact; and 
   train the one or more machine learning models using the fifth report and the sixth report.   
     
     
         7 . The system of  claim 5 , wherein:
 the first virtual environment and the second virtual environment are a same virtual environment.   
     
     
         8 . The system of  claim 5 , wherein:
 the first virtual environment and the second virtual environment are different virtual environments.   
     
     
         9 . A method, comprising:
 receiving a first plurality of tracked activities comprising one or more actions performed by a first entity in a first virtual environment over a first period of time, wherein:
 the first entity is associated with a first electronic attacker; and 
 the first virtual environment is an isolated partition of a communication network; 
   executing a machine learning algorithm configured, when executed, to analyze data in accordance with one or more machine learning models to perform one or more operations comprising:
 determining a first determined intent based on a first activity of the first plurality of tracked activities; 
 generating a first synthetic network structure based on the first determined intent, the first synthetic network structure comprising a first plurality of synthetic elements configured to resemble a first plurality of network resources; 
 assigning a first adverse impact to the first synthetic network structure; 
 placing the first synthetic network structure in the first virtual environment; 
 determining a second determined intent based on a second tracked activity of the first plurality of tracked activities; 
 generating a second synthetic network structure based on the second determined intent, the second synthetic network structure comprising a second plurality of synthetic elements configured to resemble a second plurality of network resources; 
 assigning a second adverse impact to the second synthetic network structure; and 
 placing the second synthetic network structure in the first virtual environment; 
   presenting, to the first entity, access to the first synthetic network structure and the second synthetic network structure in the first virtual environment, wherein:
 the first synthetic network structure is associated with a first divergent path in the first virtual environment; and 
 the second synthetic network structure is associated with a second divergent path in the first virtual environment; 
   determining whether the first entity performed a first action in association with the first synthetic network structure or a second action in association with the second synthetic network structure;   in response to determining that the first entity performed the first action in association with the first synthetic network structure, generating a first report comprising that the first entity is associated with the first adverse impact over the first period of time;   in response to determining that the first entity performed the second action in association with the second synthetic network structure, generating a second report comprising that the first entity is associated with the second adverse impact over the first period of time; and   training the one or more machine learning models using the first report and the second report.   
     
     
         10 . The method of  claim 9 , further comprising:
 receiving a second plurality of tracked activities comprising one or more additional actions performed by the first entity in the first virtual environment over a second period of time;   executing the machine learning algorithm to perform one or more additional operations comprising:
 determining a third determined intent based on a third activity of the second plurality of tracked activities; 
 generating a third synthetic network structure based on the third determined intent, the third synthetic network structure comprising a third plurality of synthetic elements configured to resemble a third plurality of network resources; 
 assigning a third adverse impact to the third synthetic network structure; 
 placing the third synthetic network structure in the first virtual environment; 
 determining a fourth determined intent based on a fourth tracked activity of the second plurality of tracked activities; 
 generating a fourth synthetic network structure based on the fourth determined intent, the fourth synthetic network structure comprising a fourth plurality of synthetic elements configured to resemble a fourth plurality of network resources; 
 assigning a fourth adverse impact to the fourth synthetic network structure; and 
 placing the fourth synthetic network structure in the first virtual environment; 
   presenting, to the first entity, access to the third synthetic network structure and the fourth synthetic network structure in the first virtual environment, wherein:
 the third synthetic network structure is associated with a third divergent path in the first virtual environment; and 
 the fourth synthetic network structure is associated with a fourth divergent path in the first virtual environment; 
   determining whether the first entity performed a third action in association with the third synthetic network structure or a fourth action in association with the fourth synthetic network structure;   in response to determining that the first entity performed the third action in association with the third synthetic network structure, generating a third report comprising that the first entity is associated with the third adverse impact;   in response to determining that the first entity performed the fourth action in association with the fourth synthetic network structure, generating a fourth report comprising that the first entity is associated with the fourth adverse impact; and   training the one or more machine learning models using the third report and the fourth report.   
     
     
         11 . The method of  claim 9 , further comprising:
 receiving a second plurality of tracked activities comprising one or more additional actions performed by the first entity in the first virtual environment over a second period of time;   executing the machine learning algorithm to perform one or more additional operations comprising:
 determining a third determined intent based on a third activity of the second plurality of tracked activities; 
 generating a third synthetic network structure based on the third determined intent, the third synthetic network structure comprising a third plurality of synthetic elements configured to resemble a third plurality of network resources; 
 assigning a third adverse impact to the third synthetic network structure; 
 determining a fourth determined intent based on a fourth tracked activity of the second plurality of tracked activities; 
 generating a fourth synthetic network structure based on the fourth determined intent, the fourth synthetic network structure comprising a fourth plurality of synthetic elements configured to resemble a fourth plurality of network resources; 
 assigning a fourth adverse impact to the fourth synthetic network structure; 
 generating a second virtual environment configured to resemble one or more additional portions of the communication network; and 
 placing the third synthetic network structure and the fourth synthetic network structure in the second virtual environment; 
   presenting, to the first entity, access to the third synthetic network structure and the fourth synthetic network structure in the first virtual environment, wherein:
 the third synthetic network structure is associated with a third divergent path in the second virtual environment; and 
 the fourth synthetic network structure is associated with a fourth divergent path in the second virtual environment; 
   determining whether the first entity performed a third action in association with the third synthetic network structure or a fourth action in association with the fourth synthetic network structure;   in response to determining that the first entity performed the third action in association with the third synthetic network structure, generating a third report comprising that the first entity is associated with the third adverse impact;   in response to determining that the first entity performed the fourth action in association with the fourth synthetic network structure, generating a fourth report comprising that the first entity is associated with the fourth adverse impact; and   training the one or more machine learning models using the third report and the fourth report.   
     
     
         12 . The method of  claim 11 , wherein:
 the first virtual environment is controlled by a first server associated with a first organization; and   the second virtual environment is controlled by a second server associated with a second organization.   
     
     
         13 . The method of  claim 9 , further comprising:
 receiving a second plurality of tracked activities comprising one or more additional actions performed by a second entity in a second virtual environment over a second period of time, wherein:
 the second entity is associated with a second electronic attacker; and 
 the second virtual environment is an isolated partition of a communication network; 
   executing the machine learning algorithm to perform one or more additional operations comprising:
 determining a third determined intent based on a third tracked activity of the second plurality of tracked activities; 
 generating a third synthetic network structure based on the third determined intent, the third synthetic network structure comprising a third plurality of synthetic elements configured to resemble a third plurality of network resources; 
 assigning a third adverse impact to the third synthetic network structure; 
 placing the third synthetic network structure in the second virtual environment; 
 determining a fourth determined intent based on a fourth tracked activity of the second plurality of tracked activities; 
 generating a fourth synthetic network structure based on the fourth determined intent, the fourth synthetic network structure comprising a fourth plurality of synthetic elements configured to resemble a fourth plurality of network resources; 
 assigning a fourth adverse impact to the fourth synthetic network structure; and 
 placing the fourth synthetic network structure in the second virtual environment; 
   presenting, to the second entity, access to the third synthetic network structure and the fourth synthetic network structure in the second virtual environment, wherein:
 the third synthetic network structure is associated with a third divergent path in the second virtual environment; and 
 the fourth synthetic network structure is associated with a fourth divergent path in the second virtual environment; 
   determining whether the second entity performed a third action in association with the third synthetic network structure or a fourth action in association with the fourth synthetic network structure;   in response to determining that the second entity performed the third action in association with the third synthetic network structure, generating a third report comprising that the second entity is associated with the third adverse impact over the second period of time;   in response to determining that the second entity performed the fourth action in association with the fourth synthetic network structure, generating a fourth report comprising that the second entity is associated with the fourth adverse impact over the second period of time; and   training the one or more machine learning models using the third report and the fourth report.   
     
     
         14 . The method of  claim 13 , further comprising:
 receiving a second plurality of tracked activities comprising one or more first additional actions performed by the first entity in the first virtual environment over a third period of time;   receiving a third plurality of tracked activities comprising one or more second additional actions performed by the second entity in the second virtual environment over a fourth period of time;   executing the machine learning algorithm to perform one or more additional operations comprising:
 determining a fifth determined intent based on a fifth activity of the second plurality of tracked activities; 
 generating a fifth synthetic network structure based on the fifth determined intent, the fifth synthetic network structure comprising a fifth plurality of synthetic elements configured to resemble a fifth plurality of network resources; 
 assigning a fifth adverse impact to the fifth synthetic network structure; 
 determining a sixth determined intent based on a sixth tracked activity of the second plurality of tracked activities; 
 generating a sixth synthetic network structure based on the sixth determined intent, the sixth synthetic network structure comprising a sixth plurality of synthetic elements configured to resemble a sixth plurality of network resources; 
 assigning a sixth adverse impact to the sixth synthetic network structure; and 
 generating a third virtual environment configured to resemble one or more new additional portions of the communication network; 
   placing the fifth synthetic network structure and the sixth synthetic network structure in the third virtual environment;   presenting, to the first entity, access to the fifth synthetic network structure in the third virtual environment, the fifth synthetic network structure is associated with a fifth divergent path in the third virtual environment;   presenting, to the second entity, access to the sixth synthetic network structure in the third virtual environment, the sixth synthetic network structure is associated with a sixth divergent path in the third virtual environment;   determining whether the first entity performed a fifth action in association with the fifth synthetic network structure;   in response to determining that the first entity performed the fifth action in association with the fifth synthetic network structure, generating a fifth report comprising that the first entity is associated with the fifth adverse impact;   determining whether the second entity performed a sixth action in association with the sixth synthetic network structure;   in response to determining that the second entity performed the sixth action in association with the sixth synthetic network structure, generating a sixth report comprising that the second entity is associated with the sixth adverse impact; and   training the one or more machine learning models using the fifth report and the sixth report.   
     
     
         15 . The method of  claim 13 , wherein:
 the first virtual environment and the second virtual environment are a same virtual environment.   
     
     
         16 . A non-transitory computer-readable medium storing instructions that when executed by a processor cause the processor to:
 receive a first plurality of tracked activities comprising one or more actions performed by a first entity in a first virtual environment over a first period of time, wherein:
 the first entity is associated with a first electronic attacker; and 
 the first virtual environment is an isolated partition of a communication network; 
   execute a machine learning algorithm configured, when executed, to analyze data in accordance with one or more machine learning models to:
 determine a first determined intent based on a first activity of the first plurality of tracked activities; 
 generate a first synthetic network structure based on the first determined intent, the first synthetic network structure comprising a first plurality of synthetic elements configured to resemble a first plurality of network resources; 
 assign a first adverse impact to the first synthetic network structure; 
 place the first synthetic network structure in the first virtual environment; 
 determine a second determined intent based on a second tracked activity of the first plurality of tracked activities; 
 generate a second synthetic network structure based on the second determined intent, the second synthetic network structure comprising a second plurality of synthetic elements configured to resemble a second plurality of network resources; 
 assign a second adverse impact to the second synthetic network structure; and 
 place the second synthetic network structure in the first virtual environment; 
   present, to the first entity, access to the first synthetic network structure and the second synthetic network structure in the first virtual environment, wherein:
 the first synthetic network structure is associated with a first divergent path in the first virtual environment; and 
 the second synthetic network structure is associated with a second divergent path in the first virtual environment; 
   determine whether the first entity performed a first action in association with the first synthetic network structure or a second action in association with the second synthetic network structure;   in response to determining that the first entity performed the first action in association with the first synthetic network structure, generate a first report comprising that the first entity is associated with the first adverse impact over the first period of time;   in response to determining that the first entity performed the second action in association with the second synthetic network structure, generate a second report comprising that the first entity is associated with the second adverse impact over the first period of time; and   train the one or more machine learning models using the first report and the second report.   
     
     
         17 . The non-transitory computer-readable medium of  claim 16 , wherein, when executed by the processor, the instructions further cause the processor to:
 receive a second plurality of tracked activities comprising one or more additional actions performed by the first entity in the first virtual environment over a second period of time;   execute the machine learning algorithm to:   determining a third determined intent based on a third activity of the second plurality of tracked activities;
 generate a third synthetic network structure based on the third determined intent, the third synthetic network structure comprising a third plurality of synthetic elements configured to resemble a third plurality of network resources; 
 assign a third adverse impact to the third synthetic network structure; 
 place the third synthetic network structure in the first virtual environment; 
 determine a fourth determined intent based on a fourth tracked activity of the second plurality of tracked activities; 
 generate a fourth synthetic network structure based on the fourth determined intent, the fourth synthetic network structure comprising a fourth plurality of synthetic elements configured to resemble a fourth plurality of network resources; 
 assign a fourth adverse impact to the fourth synthetic network structure; and 
 place the fourth synthetic network structure in the first virtual environment; 
   present, to the first entity, access to the third synthetic network structure and the fourth synthetic network structure in the first virtual environment, wherein:
 the third synthetic network structure is associated with a third divergent path in the first virtual environment; and 
 the fourth synthetic network structure is associated with a fourth divergent path in the first virtual environment; 
   determine whether the first entity performed a third action in association with the third synthetic network structure or a fourth action in association with the fourth synthetic network structure;   in response to determining that the first entity performed the third action in association with the third synthetic network structure, generate a third report comprising that the first entity is associated with the third adverse impact;   in response to determining that the first entity performed the fourth action in association with the fourth synthetic network structure, generate a fourth report comprising that the first entity is associated with the fourth adverse impact; and   train the one or more machine learning models using the third report and the fourth report.   
     
     
         18 . The non-transitory computer-readable medium of  claim 16 , wherein, when executed by the processor, the instructions further cause the processor to:
 receive a second plurality of tracked activities comprising one or more additional actions performed by the first entity in the first virtual environment over a second period of time;   execute the machine learning algorithm to:
 determine a third determined intent based on a third activity of the second plurality of tracked activities; 
 generate a third synthetic network structure based on the third determined intent, the third synthetic network structure comprising a third plurality of synthetic elements configured to resemble a third plurality of network resources; 
 assign a third adverse impact to the third synthetic network structure; 
 determine a fourth determined intent based on a fourth tracked activity of the second plurality of tracked activities; 
 generate a fourth synthetic network structure based on the fourth determined intent, the fourth synthetic network structure comprising a fourth plurality of synthetic elements configured to resemble a fourth plurality of network resources; 
 assign a fourth adverse impact to the fourth synthetic network structure; 
 generate a second virtual environment configured to resemble one or more additional portions of the communication network; and 
 place the third synthetic network structure and the fourth synthetic network structure in the second virtual environment; 
   present, to the first entity, access to the third synthetic network structure and the fourth synthetic network structure in the first virtual environment, wherein:
 the third synthetic network structure is associated with a third divergent path in the second virtual environment; and 
 the fourth synthetic network structure is associated with a fourth divergent path in the second virtual environment; 
   determine whether the first entity performed a third action in association with the third synthetic network structure or a fourth action in association with the fourth synthetic network structure;   in response to determining that the first entity performed the third action in association with the third synthetic network structure, generate a third report comprising that the first entity is associated with the third adverse impact;   in response to determining that the first entity performed the fourth action in association with the fourth synthetic network structure, generate a fourth report comprising that the first entity is associated with the fourth adverse impact; and   train the one or more machine learning models using the third report and the fourth report.   
     
     
         19 . The non-transitory computer-readable medium of  claim 18 , wherein:
 the first virtual environment is controlled by a first server associated with a first organization; and   the second virtual environment is controlled by a second server associated with a second organization.   
     
     
         20 . The non-transitory computer-readable medium of  claim 16 , wherein, when executed by the processor, the instructions further cause the processor to:
 receive a second plurality of tracked activities comprising one or more additional actions performed by a second entity in a second virtual environment over a second period of time, wherein:
 the second entity is associated with a second electronic attacker; and 
 the second virtual environment is an isolated partition of a communication network; 
   execute the machine learning algorithm to:
 determine a third determined intent based on a third tracked activity of the second plurality of tracked activities; 
 generate a third synthetic network structure based on the third determined intent, the third synthetic network structure comprising a third plurality of synthetic elements configured to resemble a third plurality of network resources; 
 assign a third adverse impact to the third synthetic network structure; 
 place the third synthetic network structure in the second virtual environment; 
 determine a fourth determined intent based on a fourth tracked activity of the second plurality of tracked activities; 
 generate a fourth synthetic network structure based on the fourth determined intent, the fourth synthetic network structure comprising a fourth plurality of synthetic elements configured to resemble a fourth plurality of network resources; 
 assign a fourth adverse impact to the fourth synthetic network structure; and 
 place the fourth synthetic network structure in the second virtual environment; 
   present, to the second entity, access to the third synthetic network structure and the fourth synthetic network structure in the second virtual environment, wherein:
 the third synthetic network structure is associated with a third divergent path in the second virtual environment; and 
 the fourth synthetic network structure is associated with a fourth divergent path in the second virtual environment; 
   determine whether the second entity performed a third action in association with the third synthetic network structure or a fourth action in association with the fourth synthetic network structure;   in response to determining that the second entity performed the third action in association with the third synthetic network structure, generate a third report comprising that the second entity is associated with the third adverse impact over the second period of time;   in response to determining that the second entity performed the fourth action in association with the fourth synthetic network structure, generate a fourth report comprising that the second entity is associated with the fourth adverse impact over the second period of time; and   train the one or more machine learning models using the third report and the fourth report.